CN105891546B - The method of wind vane fault diagnosis in Wind turbines yaw system based on big data - Google Patents
The method of wind vane fault diagnosis in Wind turbines yaw system based on big data Download PDFInfo
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- CN105891546B CN105891546B CN201610053599.4A CN201610053599A CN105891546B CN 105891546 B CN105891546 B CN 105891546B CN 201610053599 A CN201610053599 A CN 201610053599A CN 105891546 B CN105891546 B CN 105891546B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P21/00—Testing or calibrating of apparatus or devices covered by the preceding groups
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P13/00—Indicating or recording presence, absence, or direction, of movement
- G01P13/02—Indicating direction only, e.g. by weather vane
- G01P13/025—Indicating direction only, e.g. by weather vane indicating air data, i.e. flight variables of an aircraft, e.g. angle of attack, side slip, shear, yaw
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Abstract
The method that the present invention proposes wind vane fault diagnosis in a kind of Wind turbines yaw system based on big data, a kind of characteristic signal data acquired in the process of running based on analysis wind power generating set diagnose wind vane failure.In method for diagnosing faults of the present invention, the wind driven generator yaw correlated sequences of data collected using data acquisition unit, after the calculation processing of operation processing unit, compares related operating parameter and its respective threshold under blower current operating conditions and then judge wind vane operating status.Its very good solution is the problems of previous.
Description
Technical field：The invention belongs to wind power generation field, all wind for being related in region locating for wind power plant or wind power plant
The method of wind vane fault diagnosis in power generator yaw system diagnoses wind-driven generator wind direction more particularly to based on big data
Mark failure.
Background technique：Yaw system is one of essential composition system of horizontal shaft type wind-driven generator group.Yaw system
System mainly cooperates with the control system of wind power generating set, and the wind wheel of wind power generating set is made to be in shape windward always
State makes full use of wind energy, improves the generating efficiency of wind power generating set.
The yaw system of wind power generating set is generally divided into active yawing system and passive yawing system.For grid type wind
For power generator group, the gear drive form of active yawing is usually all used.Active yawing is referred to using electric power or hydraulic
Dragging is to complete the yawing mode to pneumatic work.
The yaw system course of work is substantially：Wind vector signal is converted to electric signal and is transmitted to yaw control processor,
Processor issues yaw clockwise or counterclockwise to yaw motor after relatively and instructs, and always wind direction is mobile for driving cabin,
Achieve the purpose that accurately to wind.In entirely yaw control process, wind direction signals are the weights of Wind turbines starting and yaw adjustment
Signal is wanted, the order of accuarcy of wind direction measurement directly affects the working efficiency of yaw system.
It is main at present that wind direction is measured using wind vane in view of the various aspects factor such as cost, service life.Due to big portion
Wind field is divided all to build the severe area of weather conditions in, the wind vane that wind direction is measured using mechanical rotation mode is easy to by dust storm
The reasons such as blocking cause to wear, so that wind direction measurement accuracy reduces；Or freeze in the lower situation of temperature, so that wind direction
The response time of measurement is too long.
Summary of the invention：
Goal of the invention：Invention provides a kind of side of wind vane fault diagnosis in the Wind turbines yaw system based on big data
Method, it is the problems of previous the purpose is to solve.
Technical solution：Invention is achieved through the following technical solutions：
The method of wind vane fault diagnosis in Wind turbines yaw system based on big data, it is characterised in that：This method
Utilize wind power plant SCADA (Supervisory Control And Data Acquisition, data acquisition and monitoring control system
System) basic information judge wind power generating set wind vane failure and operating status；It is suitable for judging grid-connected separate unit wind-force
The wind vane operating status of generating set, using the difference of separate unit blower wind direction and same group blower wind direction mean value with wind deflection threshold
Value compares, and difference is greater than wind deflection threshold value and enters wind vane breakdown judge process.
Blower wind direction data is collected in the SCADA system of Wind turbines, and offline or online data, basic information include：It is single
The wind direction and yaw position of Fans.
Using each blower terrain data in data digging method analysis wind power plant, same wind power plant inner blower is grouped.
Each blower terrain data is the longitude and latitude data in geographical location locating for blower in wind power plant.
In wind vane breakdown judge process, it is related to each operating parameter and is all made of 3 σ criterion progress parameter optimization.
The wind driven generator yaw correlated sequences of data collected using data acquisition unit, by operation processing unit
Calculation processing after, compare related operating parameter and its respective threshold under blower current operating conditions and then judge wind vane operation
State；Above-mentioned operating parameter includes：Yaw position and wind angle, data are acquired from wind power plant SCADA (Supervisory
Control And Data Acquisition, data acquisition and supervisor control) basic information；
Operating parameter threshold value includes：Wind deflection threshold value, wake effect range, blower are from determining the wind direction auto-correlation coefficient threshold
Value, blower determine the wind direction and practical wind direction cross-correlation coefficient threshold value certainly.
Above-mentioned wind deflection, blower determine the wind direction certainly and practical wind direction is wind-driven generator operating parameter through operation processing unit
It is obtained after calculation processing；In addition, the basic information of above-mentioned wind power plant SCADA comes from all blower same time models of wind power plant
Enclose interior online or off-line data；The determination of above-mentioned wake effect range need to be determined according to the practical assembling position of wind power plant.
This method will acquire data preparation into effective three-dimensional wind-powered electricity generation sequence first, and wherein wind-powered electricity generation data include from each
The survey wind data of blower sensor, yaw position data and to time series relevant with wind-powered electricity generation；In turn, according to each blower wind
Come to angle and yaw position calculating and determines the wind direction；In addition, using clustering method in big data technology, according to each blower of the whole audience
Blower classification is divided into different groups by the rugged index in locating geographical location, is taken with group blower from the mean value that determines the wind direction as practical
Wind direction；Finally, wind deflection is determining the wind direction certainly and the affiliated group of the blower practical wind direction difference calculated for each blower
Absolute value.
It using the above-mentioned each blower wind deflection obtained, determines the wind direction certainly and practical wind direction, carries out various calculation process, it is first
First, range is rejected according to each blower sector, judges whether practical wind direction is in and comes within the scope of wind direction wake effect；And it calculates each
A blower determines the wind direction and the auto-correlation and cross-correlation coefficient of practical wind direction certainly；
Wherein whether above-mentioned practical wind direction, which is in, is carried out wind direction wake effect method of determining range and is：According to hindering around place
The case where hindering object assesses the flow distortion situation in place, excludes fan-shaped region caused by significant obstacle object wake flow and picks
Except sector, it is in the rejecting Sector Range of monitoring blower if coming wind direction, whether as practical wind direction, which is in, is carried out wind direction tail
It flows in coverage, the rejecting sector of Wind turbines calculates as follows：
(1) if barrier is tall and big object
The size of barrier is equivalent to rotor diameter using formula (1-1),
In formula, the equivalent rotor diameter of De-；Lh-barrier height；Lw-barrier width (taking maximum width)；
Recycle formula (1-2) that the interference sector for influencing measuring wind speed can be acquired,
α=1.3arctan (2.5De/Le+0.15)+10 (1-2)
In formula, α-interference sector；The equivalent rotor diameter of De-；Distance of the Le-barrier away from blower；
(2) if barrier is the wind power generating set closed on
If barrier is to close on wind power generating set, interference sector directly can be acquired with formula (1-3),
α=1.3arctan (2.5Dn/Ln+0.15)+10 (1-3)
In formula, α-interference sector；Dn-closes on the rotor diameter of wind power generating set；Ln-closes on wind power generating set
Distance away from tested blower；
According to the calculated result of the flow chart of wind vane method for diagnosing faults and above-mentioned operation processing unit 13 to each wind
The wind vane operating status of machine is diagnosed, and judges wind vane fault condition；
In breakdown judge unit 14, cross-correlation coefficient calculation formula is as follows：
In formula, xi：I-th of blower accumulative 1 minute wind direction mean value is monitored in every 4 minutes；In monitoring blower 4 minutes
Wind direction mean value；
yi：Come i-th of wind direction in every 4 minutes and adds up 1 minute mean value；Machine carrys out the averaging of wind direction mean value in 4 minutes.
Auto-correlation coefficient calculation formula is as follows：
In formula, xi,N same β, i=1,2....n.
Parameter optimization is carried out to auto-correlation and cross-correlation coefficient using Pauta criterion；Concrete methods of realizing is as follows：It utilizes
Standard deviation formulaThe standard deviation of auto-correlation coefficient is calculated, wherein xiFor auto-correlation coefficient；It compares from phase
Relationship number scale records the value for being greater than 3 σ in result, and is rejected；Remaining auto-correlation coefficient after being greater than 3 σ is rejected, it is flat according to counting
Mean value formula calculates the mean value of remaining auto-correlation coefficient, and as final auto-correlation coefficient threshold value, cross-correlation coefficient it is excellent
Change process is identical as auto-correlation coefficient process.
Advantageous effect：
The method that the present invention proposes wind vane fault diagnosis in a kind of Wind turbines yaw system based on big data, it is a kind of
The characteristic signal data acquired in the process of running based on analysis wind power generating set diagnose wind vane failure.The present invention
In the method for diagnosing faults being related to, the wind driven generator yaw correlated sequences of data collected using data acquisition unit, warp
After crossing the calculation processing of operation processing unit, related operating parameter and its respective threshold are compared under blower current operating conditions in turn
Judge wind vane operating status.Its very good solution is the problems of previous.
Detailed description of the invention：
System construction drawing Fig. 1 of the invention；
Data acquisition and process flowchart Fig. 2 of the invention；
Rugged index Fig. 3 of the invention calculates schematic illustration；
Wind vane method for diagnosing faults process in wind driven generator yaw system Fig. 4 of the invention.
Specific embodiment：
Present invention is further described in detail with reference to the accompanying drawing.
As shown in Fig. 1, the present invention provides wind vane failure in a kind of Wind turbines yaw system based on big data and examines
Disconnected method, the specific implementation method for the wind vane fault diagnosis that present invention will be described in detail with reference to the accompanying is related to.
As shown in Figure 1, including data acquisition unit 11, number in wind driven generator yaw system wind vane fault diagnosis system
According to processing unit 12, operation processing unit 13, failure diagnosis unit 14.Fig. 3 shows wind vane failure of the present invention and examines
The flow chart of disconnected method.
Wherein, data acquisition unit 11 is acquired for the data of electric system, passes through parsing wind power plant and power grid
The communication protocol of SCADA and EMS (Energy Management System, competence management system), can obtain in wind power plant
The basic information of each blower.
Wind-powered electricity generation data from above-mentioned data acquisition unit 11 are carried out that comprehensive (including operation is located in advance by data processing unit 12
Reason and arrangement), process of data preprocessing is as shown in Figure 2.Data preparation will be acquired first into effective three-dimensional wind-powered electricity generation sequence.Wherein
Wind-powered electricity generation data include survey wind data from each blower sensor, yaw position data and to time sequence relevant with wind-powered electricity generation
Column.In turn, come from according to each blower wind angle and yaw position calculating and determined the wind direction；In addition, using being clustered in big data technology
Blower classification is divided into different groups according to the rugged index in geographical location locating for each blower of the whole audience, takes same group by analysis method
Blower is used as practical wind direction from the mean value that determines the wind direction.Finally, wind deflection is determining the wind direction certainly and affiliated group of the blower for each blower
The absolute value of practical wind direction difference not calculated.
About the composition of three-dimensional wind-powered electricity generation sequence, referring to table 1.In table, three-dimensional is constituted with time, wind angle and yaw position
Sequence, each column are a dimensions of data.As shown in the table, the first dimension be time, the second dimension be wind angle, last
Dimension is yaw position.
Table 1
Above-mentioned rugged index refers in certain polar coordinate system of the point using R as radius, and every radius all may be with landform etc.
High line intersection, if radius is then divided into main section by intersection point.It is more than the line segment summation of the crucial gradient with terrain slope, divided by whole
Line segment summation (radius R) just obtains the value of rugged index.Computing Principle schematic diagram is as shown in Figure 3.
Each blower wind deflection that operation processing unit 13 is obtained using above-mentioned data processing unit 12 determines the wind direction and real certainly
Border wind direction carries out various calculation process.Firstly, operation processing unit 13 rejects range according to each blower sector, reality is judged
Whether wind direction, which is in, is come within the scope of wind direction wake effect；And each blower is calculated from determining the wind direction and the auto-correlation of practical wind direction and mutual
Related coefficient.
Wherein whether above-mentioned practical wind direction, which is in, is carried out wind direction wake effect method of determining range and is：According to hindering around place
The case where hindering object assesses the flow distortion situation in place, excludes fan-shaped region caused by significant obstacle object wake flow and picks
Except sector.If coming wind direction to be in the rejecting Sector Range of monitoring blower, whether as practical wind direction, which is in, is carried out wind direction tail
It flows in coverage.The rejecting sector of Wind turbines calculates as follows：
(1) if barrier is tall and big object
The size of barrier is equivalent to rotor diameter using formula (1-1),
In formula, the equivalent rotor diameter of De-；Lh-barrier height；Lw-barrier width (taking maximum width).
Recycle formula (1-2) that the interference sector for influencing measuring wind speed can be acquired,
α=1.3arctan (2.5De/Le+0.15)+10 (1-2)
In formula, α-interference sector；The equivalent rotor diameter of De-；Distance of the Le-barrier away from blower.
(2) if barrier is the wind power generating set closed on
If barrier is to close on wind power generating set, interference sector directly can be acquired with formula (1-3),
α=1.3arctan (2.5Dn/Ln+0.15)+10 (1-3)
In formula, α-interference sector；Dn-closes on the rotor diameter of wind power generating set；Ln-closes on wind power generating set
Distance away from tested blower.
Breakdown judge unit 14 is according to the flow chart of wind vane method for diagnosing faults and the meter of above-mentioned operation processing unit 13
It calculates result to diagnose the wind vane operating status of each blower, and judges wind vane fault condition.
In breakdown judge unit 14, cross-correlation coefficient calculation formula is as follows：
In formula, xi：I-th of blower accumulative 1 minute wind direction mean value is monitored in every 4 minutes；Wind in monitoring blower 4 minutes
To mean value.
yi：Come i-th of wind direction in every 4 minutes and adds up 1 minute mean value；Machine carrys out the averaging of wind direction mean value in 4 minutes.
Auto-correlation coefficient calculation formula is as follows：
In formula, xi,N same β, i=1,2....n.
In breakdown judge unit 14, parameter optimization is carried out to auto-correlation and cross-correlation coefficient using Pauta criterion.Tool
Body implementation method is as follows：Utilize standard deviation formulaThe standard deviation of auto-correlation coefficient is calculated (wherein for certainly
Related coefficient)；It compares in auto-correlation coefficient record result and is greater than the value of 3 σ, and rejected.Reject be greater than 3 σ after it is remaining from
Related coefficient calculates the mean value of remaining auto-correlation coefficient according to arithmetic average formula, and as final auto-correlation coefficient
The optimization process of threshold value, cross-correlation coefficient is identical as auto-correlation coefficient process.
The implementation column that present invention mentioned above is related to can be ordered by the program that the computer element of multiplicity executes
Language form is enabled to realize.The example of program command language further includes using explanation journey including formed by compiling and its language codes
The higher-level language code executable on computers such as sequence.
Claims (8)
1. the method for wind vane fault diagnosis in the Wind turbines yaw system based on big data, it is characterised in that：This method benefit
Wind power generating set wind vane failure and operating status are judged with the basic information of wind power plant SCADA；It is suitable for judging simultaneously
The wind vane operating status of net single wind generator group, using separate unit blower wind direction and with group blower wind direction mean value difference with
Wind deflection threshold value compares, and difference is greater than wind deflection threshold value and enters wind vane breakdown judge process；
This method will acquire data preparation into effective three-dimensional wind-powered electricity generation sequence first, and wherein wind-powered electricity generation data include coming from each blower
The survey wind data of sensor, yaw position data and to time series relevant with wind-powered electricity generation；In turn, according to each blower wind angle
And yaw position is calculated to come from and be determined the wind direction；In addition, using clustering method in big data technology, according to locating for each blower of the whole audience
Blower classification is divided into different groups by the rugged index in geographical location, is taken and is used as practical wind from the mean value that determines the wind direction with group blower
To；Finally, wind deflection is that determining the wind direction certainly for each blower is exhausted with the affiliated group of the blower practical wind direction difference calculated
To value；
It using above-mentioned each blower wind deflection, determines the wind direction certainly and practical wind direction, various calculation process is carried out, firstly, according to each
Range is rejected in blower sector, judges whether practical wind direction is in and comes within the scope of wind direction wake effect；And each blower is calculated from survey
The auto-correlation and cross-correlation coefficient of wind direction and practical wind direction；
Wherein whether above-mentioned practical wind direction, which is in, is carried out wind direction wake effect method of determining range and is：According to place peripheral obstacle
The case where, the flow distortion situation in place is assessed, fan-shaped region caused by significant obstacle object wake flow is excluded and rejects fan
Area is in the rejecting Sector Range of monitoring blower if coming wind direction, and whether as practical wind direction, which is in, is carried out wind direction wake flow shadow
It rings in range, the rejecting sector of Wind turbines calculates as follows：
(1) if barrier is tall and big object
The size of barrier is equivalent to rotor diameter using formula (1-1),
In formula, the equivalent rotor diameter of De-；Lh-barrier height；Lw-barrier width (taking maximum width)；It is sharp again
The interference sector for influencing measuring wind speed can be acquired with formula (1-2),
α=1.3arctan (2.5De/Le+0.15)+10 (1-2)
In formula, α-interference sector；The equivalent rotor diameter of De-；Distance of the Le-barrier away from blower；
(2) if barrier is the wind power generating set closed on
If barrier is to close on wind power generating set, interference sector directly can be acquired with formula (1-3),
α=1.3arctan (2.5Dn/Ln+0.15)+10 (1-3)
In formula, α-interference sector；Dn-closes on the rotor diameter of wind power generating set；Ln-closes on wind power generating set away from quilt
Survey the distance of blower；
According to the calculated result of the flow chart of wind vane method for diagnosing faults and above-mentioned operation processing unit 13 to each blower
Wind vane operating status is diagnosed, and judges wind vane fault condition；
In breakdown judge unit 14, cross-correlation coefficient calculation formula is as follows：
In formula, xi：I-th of blower accumulative 1 minute wind direction mean value is monitored in every 4 minutes；Wind direction is equal in monitoring blower 4 minutes
Value；
yi：Come i-th of wind direction in every 4 minutes and adds up 1 minute mean value；Machine carrys out the averaging of wind direction mean value in 4 minutes；
Auto-correlation coefficient calculation formula is as follows：
In formula, xi,N same β, i=1,2....n.
2. the method for wind vane fault diagnosis in the Wind turbines yaw system according to claim 1 based on big data,
It is characterized in that：Blower wind direction data is collected in the SCADA system of Wind turbines, and offline or online data, basic information include：
The wind direction and yaw position of separate unit blower.
3. the method for wind vane fault diagnosis in the Wind turbines yaw system according to claim 1 based on big data,
It is characterized in that：Using each blower terrain data in data digging method analysis wind power plant, same wind power plant inner blower is grouped.
4. the method for wind vane fault diagnosis in the Wind turbines yaw system according to claim 1 based on big data,
It is characterized in that：Each blower terrain data is the longitude and latitude data in geographical location locating for blower in wind power plant.
5. the method for wind vane fault diagnosis in the Wind turbines yaw system according to claim 1 based on big data,
It is characterized in that：In wind vane breakdown judge process, it is related to each operating parameter and is all made of 3 σ criterion progress parameter optimization.
6. the method for wind vane fault diagnosis in the Wind turbines yaw system according to claim 1 based on big data,
It is characterized in that：The wind driven generator yaw correlated sequences of data collected using data acquisition unit, by calculation process
After the calculation processing of unit, compares related operating parameter and its respective threshold under blower current operating conditions and then judge wind vane
Operating status；Above-mentioned operating parameter includes：Yaw position and wind angle, data are acquired from wind power plant SCADA (Supervisory
Control And Data Acquisition, data acquisition and supervisor control) basic information；
Operating parameter threshold value includes：Wind deflection threshold value, wake effect range, blower are from determining the wind direction auto-correlation coefficient threshold value, wind
Machine determines the wind direction and practical wind direction cross-correlation coefficient threshold value certainly.
7. the method for wind vane fault diagnosis in the Wind turbines yaw system according to claim 1 based on big data,
It is characterized in that：Above-mentioned wind deflection, blower determine the wind direction certainly and practical wind direction is wind-driven generator operating parameter through calculation process
It is obtained after unit calculation processing；In addition, the basic information of above-mentioned wind power plant SCADA come from all blowers of wind power plant it is identical when
Between online or off-line data in range；The determination of above-mentioned wake effect range need to be true according to the practical assembling position of wind power plant
It is fixed.
8. the method for wind vane fault diagnosis in the Wind turbines yaw system according to claim 1 based on big data,
It is characterized in that：Parameter optimization is carried out to auto-correlation and cross-correlation coefficient using Pauta criterion；Concrete methods of realizing is as follows：Benefit
With standard deviation formulaThe standard deviation of auto-correlation coefficient is calculated, wherein xiFor auto-correlation coefficient；Comparison is certainly
Related coefficient records the value for being greater than 3 σ in result, and is rejected；Remaining auto-correlation coefficient after being greater than 3 σ is rejected, according to counting
Mean Value Formulas calculates the mean value of remaining auto-correlation coefficient, and as final auto-correlation coefficient threshold value, cross-correlation coefficient
Optimization process is identical as auto-correlation coefficient process.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN87206352U (en) * | 1987-04-09 | 1987-12-02 | 云南省曲靖地区气象处 | El type electric wind fault detector |
| CN102353814A (en) * | 2011-06-30 | 2012-02-15 | 江苏省无线电科学研究所有限公司 | Method for detecting output signal failure of wind direction sensor |
| CN102418661A (en) * | 2011-12-21 | 2012-04-18 | 上海电机学院 | Fault diagnosis method for yaw system of wind driven generator |
| CN102520211A (en) * | 2011-12-31 | 2012-06-27 | 山东省科学院海洋仪器仪表研究所 | Fault detection device for Gray code disk wind-direction sensor |
| CN103020462A (en) * | 2012-12-21 | 2013-04-03 | 华北电力大学 | Wind power plant probability output power calculation method considering complex wake effect model |
| CA2876072A1 (en) * | 2012-06-15 | 2013-12-19 | Wobben Properties Gmbh | Microwave and/or radar systems in wind turbines |
| CN105041570A (en) * | 2015-07-30 | 2015-11-11 | 北京天诚同创电气有限公司 | Yaw control method and device for wind turbine generator |
-
2016
- 2016-01-26 CN CN201610053599.4A patent/CN105891546B/en not_active Expired - Fee Related
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN87206352U (en) * | 1987-04-09 | 1987-12-02 | 云南省曲靖地区气象处 | El type electric wind fault detector |
| CN102353814A (en) * | 2011-06-30 | 2012-02-15 | 江苏省无线电科学研究所有限公司 | Method for detecting output signal failure of wind direction sensor |
| CN102418661A (en) * | 2011-12-21 | 2012-04-18 | 上海电机学院 | Fault diagnosis method for yaw system of wind driven generator |
| CN102520211A (en) * | 2011-12-31 | 2012-06-27 | 山东省科学院海洋仪器仪表研究所 | Fault detection device for Gray code disk wind-direction sensor |
| CA2876072A1 (en) * | 2012-06-15 | 2013-12-19 | Wobben Properties Gmbh | Microwave and/or radar systems in wind turbines |
| CN103020462A (en) * | 2012-12-21 | 2013-04-03 | 华北电力大学 | Wind power plant probability output power calculation method considering complex wake effect model |
| CN105041570A (en) * | 2015-07-30 | 2015-11-11 | 北京天诚同创电气有限公司 | Yaw control method and device for wind turbine generator |
Non-Patent Citations (3)
| Title |
|---|
| "利用集合理论定位判定风向传感器故障";何利德 等;《气象水文海洋仪器》;20090331(第1期);第69-73页 * |
| "基于观测数据的风向传感器故障检测方法设计与应用";刘莹 等;《气象》;20151130;第41卷(第11期);第1408-1411页 * |
| "风向数据在判断传感器故障方面的分析释用";杨丽中 等;《气象水文海洋仪器》;20100630(第2期);第86-90页 * |
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